scholarly journals Eleven-year Correlation of Physical Fruit Texture Traits between Computerized Penetrometers and Sensory Assessment in an Apple Breeding Program

2020 ◽  
Vol 30 (6) ◽  
pp. 719-724
Author(s):  
Soon Li Teh ◽  
Lisa Brutcher ◽  
Bonnie Schonberg ◽  
Kate Evans

Fruit texture is a major target of apple (Malus domestica) breeding programs due to its influence on consumer preference. This multitrait feature is typically rated using sensory assessment, which is subjective and prone to biases. Instrumental measurements have predominantly targeted firmness of the outer region of fruit cortex using industry standard Magness–Taylor-type penetrometers, while other metrics remain largely unused. Additionally, there have been limited reports on correlating sensory attributes with instrumental metrics on many diverse apple selections. This report is the first to correlate multiyear historical fruit texture information of instrumental metrics and sensory assessment in an apple breeding program. Through 11 years of routine fruit quality evaluation at the Washington State University apple breeding program, physical textural data of 84,552 fruit acquired from computerized penetrometers were correlated with sensory assessment. Correlations among various instrumental metrics are high (0.63 ≤ r ≤ 1.00; P < 0.0001). In correlating instrumental outputs with sensory data, there is a significant correlation (r = 0.43; P < 0.0001) between the instrumental crispness value and sensory crispness. Additionally, instrumental hardness traits are significantly correlated (0.61 ≤ r ≤ 0.69; P < 0.0001) with sensory hardness. Outputs from two versions of computerized penetrometers were tested and shown to have no statistical differences. Overall, this report demonstrates potential use of instrumental metrics as firmness and crispness estimates for selecting apples of diverse backgrounds in a breeding program. However, in testing a large number and diversity of fruit, experimenters should perform data curation and account for lower limits/thresholds of the instrument.

2010 ◽  
Vol 20 (6) ◽  
pp. 1026-1029 ◽  
Author(s):  
Kate Evans ◽  
Lisa Brutcher ◽  
Bonnie Konishi ◽  
Bruce Barritt

Selecting for crispness instrumentally in fruit from apple (Malus ×domestica) breeding programs is notoriously difficult. Most breeders rely on sensory assessment for this important characteristic. Following the 2009 harvest, we used a computerized penetrometer to assess firmness and texture of apple selections from the Washington State University's apple breeding program and 16 standard reference varieties. Data were compared with sensory data from the apple breeding team. In addition to the expected high correlations between the various firmness measures of the computerized penetrometer and the sensory firmness values, our data also show a significant correlation between the computerized penetrometer crispness value and the sensory crispness value, thus demonstrating the benefit from using this equipment rather than the industry standard Magness–Taylor penetrometer.


2021 ◽  
Vol 12 ◽  
Author(s):  
Soon Li Teh ◽  
Sarah Kostick ◽  
Lisa Brutcher ◽  
Bonnie Schonberg ◽  
Bruce Barritt ◽  
...  

Washington State University's apple breeding program (WABP) was initiated in 1994 to select new apple cultivars with improved eating quality, appearance, and storability that are suitable for production in the main growing regions of the state. Fruit quality is phenotyped using various instrumental measures, such as penetrometers (texture), titrator (acidity), and refractometer (soluble solids concentration; SSC), as well as sensory assessment. The selection regime of WABP occurs in three sequential phases: phase one (P1)—single, unreplicated seedlings at one site, phase two (P2)—replicated selections at three geographically diverse sites, and phase three (P3)—highly replicated elite selections at one to two grower sites. Most of the data collection of WABP occurs in P2. Knowledge of trends/changes associated with advancing selections is essential for understanding the selection criteria and progress of WABP throughout the changing compositions of advancing and culling selections. For each post-harvest trait, P2 data from harvest years 2005 to 2019 were split across sites, and between selections and reference cultivars (e.g., Cripps Pink, Gala, and Honeycrisp). Means of instrumental crispness (Cn) and inner cortex firmness for the advancing selections increased gradually over this period and were significantly higher than those for cultivars. Means of outer cortex firmness measurements were stable for selections but significantly higher than those for cultivars. The average fruit acidity of selections increased marginally over this period and was higher than that of the cultivars. Meanwhile, the average fruit SSCs of selections and cultivars were statistically indistinguishable. These 15-year trends indicate that WABP has been selecting apples with improved eating quality and storability through increased crispness and inner cortex firmness, respectively.


Foods ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 98 ◽  
Author(s):  
Teodora Basile ◽  
Antonio Domenico Marsico ◽  
Maria Francesca Cardone ◽  
Donato Antonacci ◽  
Rocco Perniola

Fourier-transform near infrared spectroscopy (FT-NIR) is a technique used in the compositional and sensory analysis of foodstuffs. In this work, we have measured the main maturity parameters for grape (sugars and acids) using hundreds of intact berry samples to build models for the prediction of these parameters from berries of two very different varieties: “Victoria” and “Autumn Royal”. Together with the chemical composition in terms of sugar and acidic content, we have carried out a sensory analysis on single berries. Employing the models built for sugars and acids it was possible to learn the sweetness and acidity of each berry before the destructive sensory analysis. The direct correlation of sensory data with FT-NIR spectra is difficult; therefore, spectral data were exported from the spectrometer built-in software and analyzed with R software using a statistical analysis technique (Spearman correlation) which allowed the correlation of berry appreciation data with specific wavelengths that were then related to sugar and acidic content. In this article, we show how it is possible to carry out the analysis of single berries to obtain data on chemical composition parameters and consumer appreciation with a fast, simple, and non-destructive technique with a clear advantage for producers and consumers.


HortScience ◽  
2012 ◽  
Vol 47 (1) ◽  
pp. 149
Author(s):  
Kate M. Evans ◽  
Lisa J. Brutcher ◽  
Bonnie S. Konishi

Inventory control of trees and fruit samples in the Washington State University apple breeding program has been simplified by the use of bar codes. Tree labels incorporate individual bar-coded identities that can be scanned in the field when taking measurements or collecting samples. Bar codes on fruit sample labels also simplify data recording as well as improve the efficiency of the program by greatly reducing the risk of errors. The interface of bar-code identities with data organization and statistical software makes data analysis more straightforward.


2020 ◽  
Author(s):  
Zhen Fan ◽  
Tomas Hasing ◽  
Timothy S. Johnson ◽  
Drake M. Garner ◽  
Christopher R. Barbey ◽  
...  

ABSTRACTBreeding crops for improved flavor is challenging due to the high cost of sensory evaluation and the difficulty of connecting sensory experience to chemical composition. The main goal of this study was to identify the chemical drivers of sweetness and consumer liking for fresh strawberries (Fragaria ×ananassa). Fruit of 148 strawberry samples from cultivars and breeding selections were grown and harvested over seven years and were subjected to both sensory and chemical analyses. Each panel consisted of at least 100 consumers, resulting in more than 15,000 sensory data points per descriptor. Three sugars, two acids and 113 volatile compounds were quantified. Consumer liking was highly associated with sweetness intensity, texture liking, and flavor intensity, but not sourness intensity. Partial least square analyses revealed 20 volatile compounds that increased sweetness perception independently of sugars; 18 volatiles that increased liking independently of sugars; and 15 volatile compounds that had positive effects on both. Machine learning-based predictive models including sugars, acids, and volatiles explained at least 25% more variation in sweetness and liking than models accounting for sugars and acids only. Volatile compounds such as γ-dodecalactone; 5-hepten-2-one, 6-methyl; and multiple medium-chain fatty acid esters may serve as targets for breeding or quality control attributes for strawberry products. A genetic association study identified two loci controlling ester production, both on linkage group 6A. Co-segregating makers in these regions can be used for increasing multiple esters simultaneously. This study demonstrates a paradigm for improvement of fruit sweetness and flavor in which consumers drive the identification of the most important chemical targets, which in turn drives the discovery of genetic targets for marker-assisted breeding.


HortScience ◽  
2006 ◽  
Vol 41 (1) ◽  
pp. 8-10 ◽  
Author(s):  
Jules Janick

2000 ◽  
pp. 715-718 ◽  
Author(s):  
S. Khanizadeh ◽  
Y. Grzyb ◽  
J. Cousineau ◽  
R. Granger ◽  
G. Rousselle

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